
# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask


# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")


nltk.download('all')

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# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_tvshows = pd.read_csv(path + 'otttvshows.csv')
 
df_tvshows.head()


# profile = ProfileReport(df_tvshows)
# profile


def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()


data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                1954
IMDb                556
Rotten Tomatoes    4194
Directors          5158
Cast                486
Genres              323
Country             549
Language            638
Plotline           2493
Runtime            1410
Seasons             679
dtype: int64
**************************************************
Missing vaules %age wise :

ID                  0.000000
Title               0.000000
Year                0.000000
Age                35.972018
IMDb               10.235641
Rotten Tomatoes    77.209131
Directors          94.955817
Cast                8.946981
Genres              5.946244
Country            10.106775
Language           11.745214
Plotline           45.894698
Runtime            25.957290
Kind                0.000000
Seasons            12.500000
Netflix             0.000000
Hulu                0.000000
Prime Video         0.000000
Disney+             0.000000
Type                0.000000
dtype: float64
**************************************************
Pictorial Representation :


# ID
# df_tvshows = df_tvshows.drop(['ID'], axis = 1)
 
# Age
df_tvshows.loc[df_tvshows['Age'].isnull() & df_tvshows['Disney+'] == 1, "Age"] = '13'
# df_tvshows.fillna({'Age' : 18}, inplace = True)
df_tvshows.fillna({'Age' : 'NR'}, inplace = True)
df_tvshows['Age'].replace({'all': '0'}, inplace = True)
df_tvshows['Age'].replace({'7+': '7'}, inplace = True)
df_tvshows['Age'].replace({'13+': '13'}, inplace = True)
df_tvshows['Age'].replace({'16+': '16'}, inplace = True)
df_tvshows['Age'].replace({'18+': '18'}, inplace = True)
# df_tvshows['Age'] = df_tvshows['Age'].astype(int)
 
# IMDb
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].mean()}, inplace = True)
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].median()}, inplace = True)
df_tvshows.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].astype(int)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].mean()}, inplace = True)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].median()}, inplace = True)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'].astype(int)
df_tvshows.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_tvshows = df_tvshows.drop(['Directors'], axis = 1)
df_tvshows.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_tvshows.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_tvshows.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_tvshows.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_tvshows.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_tvshows.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_tvshows.fillna({'Runtime' : df_tvshows['Runtime'].mean()}, inplace = True)
# df_tvshows['Runtime'] = df_tvshows['Runtime'].astype(int)
df_tvshows.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_tvshows.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_tvshows.fillna({'Type': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Type'], axis = 1)
 
# Seasons
# df_tvshows.fillna({'Seasons': 1}, inplace = True)
df_tvshows.fillna({'Seasons': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Seasons'], axis = 1)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# df_tvshows.fillna({'Seasons' : df_tvshows['Seasons'].mean()}, inplace = True)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
 
# Service Provider
df_tvshows['Service Provider'] = df_tvshows.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_tvshows.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_tvshows.dropna(how = 'any', inplace = True)
df_tvshows.drop_duplicates(inplace = True)


data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  21
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Seasons             object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Seasons             0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation :


df_tvshows.head()


df_tvshows.describe()


df_tvshows.corr()


# df_tvshows.sort_values('Year', ascending = True)
# df_tvshows.sort_values('IMDb', ascending = False)


# df_tvshows.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_otttvshows.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_tvshows = pd.read_csv(path + 'updated_otttvshows.csv')
 
# udf_tvshows


# df_netflix_tvshows = df_tvshows.loc[(df_tvshows['Netflix'] > 0)]
# df_hulu_tvshows = df_tvshows.loc[(df_tvshows['Hulu'] > 0)]
# df_prime_video_tvshows = df_tvshows.loc[(df_tvshows['Prime Video'] > 0)]
# df_disney_tvshows = df_tvshows.loc[(df_tvshows['Disney+'] > 0)]


df_netflix_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 1) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_hulu_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 1) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_prime_video_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 1 ) & (df_tvshows['Disney+'] == 0)]
df_disney_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 1)]


df_tvshows_languages = df_tvshows.copy()


df_tvshows_languages.drop(df_tvshows_languages.loc[df_tvshows_languages['Language'] == "NA"].index, inplace = True)
# df_tvshows_languages = df_tvshows_languages[df_tvshows_languages.Language != "NA"]
# df_tvshows_languages['Language'] = df_tvshows_languages['Language'].astype(str)


df_tvshows_count_languages = df_tvshows_languages.copy()


df_tvshows_language = df_tvshows_languages.copy()


# Create languages dict where key=name and value = number of languages
 
languages = {}
 
for i in df_tvshows_count_languages['Language'].dropna():
    if i != "NA":
        #print(i,len(i.split(',')))
        languages[i] = len(i.split(','))
    else:
        languages[i] = 0
    
# Add this information to our dataframe as a new column
 
df_tvshows_count_languages['Number of Languages'] = df_tvshows_count_languages['Language'].map(languages).astype(int)


df_tvshows_mixed_languages = df_tvshows_count_languages.copy()


# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_languages_tvshows = df_tvshows_count_languages.loc[df_tvshows_count_languages['Netflix'] == 1]
hulu_languages_tvshows = df_tvshows_count_languages.loc[df_tvshows_count_languages['Hulu'] == 1]
prime_video_languages_tvshows = df_tvshows_count_languages.loc[df_tvshows_count_languages['Prime Video'] == 1]
disney_languages_tvshows = df_tvshows_count_languages.loc[df_tvshows_count_languages['Disney+'] == 1]


plt.figure(figsize = (10, 10))
corr = df_tvshows_count_languages.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, alleast annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>


df_languages_most_tvshows = df_tvshows_count_languages.sort_values(by = 'Number of Languages', ascending = False).reset_index()
df_languages_most_tvshows = df_languages_most_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_count_languages['Number of Languages'] == (df_tvshows_count_languages['Number of Languages'].max()))
# df_languages_most_tvshows = df_tvshows_count_languages[filter]
 
# mostest_rated_tvshows = df_tvshows_count_languages.loc[df_tvshows_count_languages['Number of Languages'].idxmax()]
 
print('\nTV Shows with Highest Ever Number of Languages are : \n')
df_languages_most_tvshows.head(5)

TV Shows with Highest Ever Number of Languages are :


fig = px.bar(y = df_languages_most_tvshows['Title'][:15],
             x = df_languages_most_tvshows['Number of Languages'][:15], 
             color = df_languages_most_tvshows['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Languages'},
             title  = 'TV Shows with Highest Number of Languages : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_languages_least_tvshows = df_tvshows_count_languages.sort_values(by = 'Number of Languages', ascending = True).reset_index()
df_languages_least_tvshows = df_languages_least_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_count_languages['Number of Languages'] == (df_tvshows_count_languages['Number of Languages'].min()))
# df_languages_least_tvshows = df_tvshows_count_languages[filter]

print('\nTV Shows with Lowest Ever Number of Languages are : \n')
df_languages_least_tvshows.head(5)

TV Shows with Lowest Ever Number of Languages are :


fig = px.bar(y = df_languages_least_tvshows['Title'][:15],
             x = df_languages_least_tvshows['Number of Languages'][:15], 
             color = df_languages_least_tvshows['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Languages'},
             title  = 'TV Shows with Lowest Number of Languages : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      Total '{df_tvshows_count_languages['Number of Languages'].unique().shape[0]}' unique Number of Languages s were Given, They were Like this,\n
      
      {df_tvshows_count_languages.sort_values(by = 'Number of Languages', ascending = False)['Number of Languages'].unique()}\n
 
      The Highest Number of Languages Ever Any TV Show Got is '{df_languages_most_tvshows['Title'][0]}' : '{df_languages_most_tvshows['Number of Languages'].max()}'\n
 
      The Lowest Number of Languages Ever Any TV Show Got is '{df_languages_least_tvshows['Title'][0]}' : '{df_languages_least_tvshows['Number of Languages'].min()}'\n
      ''')

      Total '16' unique Number of Languages s were Given, They were Like this,

      
      [20 18 16 14 12 11 10  9  8  7  6  5  4  3  2  1]

 
      The Highest Number of Languages Ever Any TV Show Got is 'The Simpsons' : '20'

 
      The Lowest Number of Languages Ever Any TV Show Got is 'Snowpiercer' : '1'


netflix_languages_most_tvshows = df_languages_most_tvshows.loc[df_languages_most_tvshows['Netflix']==1].reset_index()
netflix_languages_most_tvshows = netflix_languages_most_tvshows.drop(['index'], axis = 1)
 
netflix_languages_least_tvshows = df_languages_least_tvshows.loc[df_languages_least_tvshows['Netflix']==1].reset_index()
netflix_languages_least_tvshows = netflix_languages_least_tvshows.drop(['index'], axis = 1)
 
netflix_languages_most_tvshows.head(5)


fig = px.bar(y = netflix_languages_most_tvshows['Title'][:15],
             x = netflix_languages_most_tvshows['Number of Languages'][:15], 
             color = netflix_languages_most_tvshows['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Languages'},
             title  = 'TV Shows with Highest Number of Languages : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = netflix_languages_least_tvshows['Title'][:15],
             x = netflix_languages_least_tvshows['Number of Languages'][:15], 
             color = netflix_languages_least_tvshows['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Languages'},
             title  = 'TV Shows with Lowest Number of Languages : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


hulu_languages_most_tvshows = df_languages_most_tvshows.loc[df_languages_most_tvshows['Hulu']==1].reset_index()
hulu_languages_most_tvshows = hulu_languages_most_tvshows.drop(['index'], axis = 1)
 
hulu_languages_least_tvshows = df_languages_least_tvshows.loc[df_languages_least_tvshows['Hulu']==1].reset_index()
hulu_languages_least_tvshows = hulu_languages_least_tvshows.drop(['index'], axis = 1)
 
hulu_languages_most_tvshows.head(5)


fig = px.bar(y = hulu_languages_most_tvshows['Title'][:15],
             x = hulu_languages_most_tvshows['Number of Languages'][:15], 
             color = hulu_languages_most_tvshows['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Languages'},
             title  = 'TV Shows with Highest Number of Languages : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = hulu_languages_least_tvshows['Title'][:15],
             x = hulu_languages_least_tvshows['Number of Languages'][:15], 
             color = hulu_languages_least_tvshows['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Languages'},
             title  = 'TV Shows with Lowest Number of Languages : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


prime_video_languages_most_tvshows = df_languages_most_tvshows.loc[df_languages_most_tvshows['Prime Video']==1].reset_index()
prime_video_languages_most_tvshows = prime_video_languages_most_tvshows.drop(['index'], axis = 1)
 
prime_video_languages_least_tvshows = df_languages_least_tvshows.loc[df_languages_least_tvshows['Prime Video']==1].reset_index()
prime_video_languages_least_tvshows = prime_video_languages_least_tvshows.drop(['index'], axis = 1)
 
prime_video_languages_most_tvshows.head(5)


fig = px.bar(y = prime_video_languages_most_tvshows['Title'][:15],
             x = prime_video_languages_most_tvshows['Number of Languages'][:15], 
             color = prime_video_languages_most_tvshows['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Languages'},
             title  = 'TV Shows with Highest Number of Languages : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = prime_video_languages_least_tvshows['Title'][:15],
             x = prime_video_languages_least_tvshows['Number of Languages'][:15], 
             color = prime_video_languages_least_tvshows['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Languages'},
             title  = 'TV Shows with Lowest Number of Languages : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


disney_languages_most_tvshows = df_languages_most_tvshows.loc[df_languages_most_tvshows['Disney+']==1].reset_index()
disney_languages_most_tvshows = disney_languages_most_tvshows.drop(['index'], axis = 1)
 
disney_languages_least_tvshows = df_languages_least_tvshows.loc[df_languages_least_tvshows['Disney+']==1].reset_index()
disney_languages_least_tvshows = disney_languages_least_tvshows.drop(['index'], axis = 1)
 
disney_languages_most_tvshows.head(5)


fig = px.bar(y = disney_languages_most_tvshows['Title'][:15],
             x = disney_languages_most_tvshows['Number of Languages'][:15], 
             color = disney_languages_most_tvshows['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Languages'},
             title  = 'TV Shows with Highest Number of Languages : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = disney_languages_least_tvshows['Title'][:15],
             x = disney_languages_least_tvshows['Number of Languages'][:15], 
             color = disney_languages_least_tvshows['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Languages'},
             title  = 'TV Shows with Lowest Number of Languages : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      The TV Show with Highest Number of Languages Ever Got is '{df_languages_most_tvshows['Title'][0]}' : '{df_languages_most_tvshows['Number of Languages'].max()}'\n
      The TV Show with Lowest Number of Languages Ever Got is '{df_languages_least_tvshows['Title'][0]}' : '{df_languages_least_tvshows['Number of Languages'].min()}'\n
      
      The TV Show with Highest Number of Languages on 'Netflix' is '{netflix_languages_most_tvshows['Title'][0]}' : '{netflix_languages_most_tvshows['Number of Languages'].max()}'\n
      The TV Show with Lowest Number of Languages on 'Netflix' is '{netflix_languages_least_tvshows['Title'][0]}' : '{netflix_languages_least_tvshows['Number of Languages'].min()}'\n
      
      The TV Show with Highest Number of Languages on 'Hulu' is '{hulu_languages_most_tvshows['Title'][0]}' : '{hulu_languages_most_tvshows['Number of Languages'].max()}'\n
      The TV Show with Lowest Number of Languages on 'Hulu' is '{hulu_languages_least_tvshows['Title'][0]}' : '{hulu_languages_least_tvshows['Number of Languages'].min()}'\n
      
      The TV Show with Highest Number of Languages on 'Prime Video' is '{prime_video_languages_most_tvshows['Title'][0]}' : '{prime_video_languages_most_tvshows['Number of Languages'].max()}'\n
      The TV Show with Lowest Number of Languages on 'Prime Video' is '{prime_video_languages_least_tvshows['Title'][0]}' : '{prime_video_languages_least_tvshows['Number of Languages'].min()}'\n
      
      The TV Show with Highest Number of Languages on 'Disney+' is '{disney_languages_most_tvshows['Title'][0]}' : '{disney_languages_most_tvshows['Number of Languages'].max()}'\n
      The TV Show with Lowest Number of Languages on 'Disney+' is '{disney_languages_least_tvshows['Title'][0]}' : '{disney_languages_least_tvshows['Number of Languages'].min()}'\n 
      ''')

      The TV Show with Highest Number of Languages Ever Got is 'The Simpsons' : '20'

      The TV Show with Lowest Number of Languages Ever Got is 'Snowpiercer' : '1'

      
      The TV Show with Highest Number of Languages on 'Netflix' is 'Legend Quest' : '18'

      The TV Show with Lowest Number of Languages on 'Netflix' is 'Snowpiercer' : '1'

      
      The TV Show with Highest Number of Languages on 'Hulu' is 'The Simpsons' : '20'

      The TV Show with Lowest Number of Languages on 'Hulu' is 'Cultureshock' : '1'

      
      The TV Show with Highest Number of Languages on 'Prime Video' is 'Vikings' : '10'

      The TV Show with Lowest Number of Languages on 'Prime Video' is 'New York Goes to Work' : '1'

      
      The TV Show with Highest Number of Languages on 'Disney+' is 'The Simpsons' : '20'

      The TV Show with Lowest Number of Languages on 'Disney+' is 'Lost Treasures of Egypt' : '1'


print(f'''
      Accross All Platforms the Average Number of Languages is '{round(df_tvshows_count_languages['Number of Languages'].mean(), ndigits = 2)}'\n
      The Average Number of Languages on 'Netflix' is '{round(netflix_languages_tvshows['Number of Languages'].mean(), ndigits = 2)}'\n
      The Average Number of Languages on 'Hulu' is '{round(hulu_languages_tvshows['Number of Languages'].mean(), ndigits = 2)}'\n
      The Average Number of Languages on 'Prime Video' is '{round(prime_video_languages_tvshows['Number of Languages'].mean(), ndigits = 2)}'\n
      The Average Number of Languages on 'Disney+' is '{round(disney_languages_tvshows['Number of Languages'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Number of Languages is '1.2'

      The Average Number of Languages on 'Netflix' is '1.24'

      The Average Number of Languages on 'Hulu' is '1.23'

      The Average Number of Languages on 'Prime Video' is '1.16'

      The Average Number of Languages on 'Disney+' is '1.34'


print(f'''
      Accross All Platforms Total Count of Language is '{df_tvshows_count_languages['Number of Languages'].max()}'\n
      Total Count of Language on 'Netflix' is '{netflix_languages_tvshows['Number of Languages'].max()}'\n
      Total Count of Language on 'Hulu' is '{hulu_languages_tvshows['Number of Languages'].max()}'\n
      Total Count of Language on 'Prime Video' is '{prime_video_languages_tvshows['Number of Languages'].max()}'\n
      Total Count of Language on 'Disney+' is '{disney_languages_tvshows['Number of Languages'].max()}'\n 
      ''')

      Accross All Platforms Total Count of Language is '20'

      Total Count of Language on 'Netflix' is '18'

      Total Count of Language on 'Hulu' is '20'

      Total Count of Language on 'Prime Video' is '10'

      Total Count of Language on 'Disney+' is '20'


f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_tvshows_count_languages['Number of Languages'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_tvshows_count_languages['Number of Languages'], ax = ax[1])
plt.show()


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Number of Languages s Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_languages_tvshows['Number of Languages'], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_languages_tvshows['Number of Languages'], color = 'red', legend = True, kde = True)
sns.histplot(hulu_languages_tvshows['Number of Languages'], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_languages_tvshows['Number of Languages'], color = 'darkblue', legend = True, kde = True) 
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()


df_lan = df_tvshows_language['Language'].str.split(',').apply(pd.Series).stack()
del df_tvshows_language['Language']
df_lan.index = df_lan.index.droplevel(-1)
df_lan.name = 'Language'
df_tvshows_language = df_tvshows_language.join(df_lan)
df_tvshows_language.drop_duplicates(inplace = True)


df_tvshows_language.head(5)


language_count = df_tvshows_language.groupby('Language')['Title'].count()
language_tvshows = df_tvshows_language.groupby('Language')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
language_data_tvshows = pd.concat([language_count, language_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count'})
language_data_tvshows = language_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)


# Language with TV Shows Counts - All Platforms Combined
language_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)[:10]


fig = px.bar(x = language_data_tvshows['Language'][:50],
             y = language_data_tvshows['TV Shows Count'][:50], 
             color = language_data_tvshows['TV Shows Count'][:50],
             color_continuous_scale = 'Teal_r', 
             labels = { 'x' : 'Language', 'y' : 'TV Shows Count'},
             title  = 'Major Languages : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_language_high_tvshows = language_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_language_high_tvshows = df_language_high_tvshows.drop(['index'], axis = 1)
# filter = (language_data_tvshows['TV Shows Count'] == (language_data_tvshows['TV Shows Count'].max()))
# df_language_high_tvshows = language_data_tvshows[filter]
 
# highest_rated_tvshows = language_data_tvshows.loc[language_data_tvshows['TV Shows Count'].idxmax()]
 
print('\nLanguage with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_language_high_tvshows.head(5)

Language with Highest Ever TV Shows Count are : All Platforms Combined


fig = px.bar(y = df_language_high_tvshows['Language'][:15],
             x = df_language_high_tvshows['TV Shows Count'][:15], 
             color = df_language_high_tvshows['TV Shows Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Language', 'x' : 'TV Shows Count'},
             title  = 'Language with Highest TV Shows : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_language_low_tvshows = language_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_language_low_tvshows = df_language_low_tvshows.drop(['index'], axis = 1)
# filter = (language_data_tvshows['TV Shows Count'] == (language_data_tvshows['TV Shows Count'].min()))
# df_language_low_tvshows = language_data_tvshows[filter]

print('\nLanguage with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_language_low_tvshows.head(5)

Language with Lowest Ever TV Shows Count are : All Platforms Combined


fig = px.bar(y = df_language_low_tvshows['Language'][:15],
             x = df_language_low_tvshows['TV Shows Count'][:15], 
             color = df_language_low_tvshows['TV Shows Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Language', 'x' : 'TV Shows Count'},
             title  = 'Language with Lowest TV Shows Count : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      Total '{language_data_tvshows['Language'].unique().shape[0]}' unique Language Count s were Given, They were Like this,\n
      
      {language_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['Language'].unique()[:5]}\n
 
      The Highest Ever TV Shows Count Ever Any TV Show Got is '{df_language_high_tvshows['Language'][0]}' : '{df_language_high_tvshows['TV Shows Count'].max()}'\n
 
      The Lowest Ever TV Shows Count Ever Any TV Show Got is '{df_language_low_tvshows['Language'][0]}' : '{df_language_low_tvshows['TV Shows Count'].min()}'\n
      ''')

      Total '101' unique Language Count s were Given, They were Like this,

      
      ['English' 'Japanese' 'Spanish' 'French' 'Korean']

 
      The Highest Ever TV Shows Count Ever Any TV Show Got is 'English' : '3876'

 
      The Lowest Ever TV Shows Count Ever Any TV Show Got is ' Ancient (to 1453)' : '1'


fig = px.pie(language_data_tvshows[:10], names = 'Language', values = 'TV Shows Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'TV Shows Count based on Language')
fig.show()


# netflix_language_tvshows = language_data_tvshows[language_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_language_tvshows = netflix_language_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
netflix_language_high_tvshows = df_language_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_language_high_tvshows = netflix_language_high_tvshows.drop(['index'], axis = 1)
 
netflix_language_low_tvshows = df_language_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_language_low_tvshows = netflix_language_low_tvshows.drop(['index'], axis = 1)
 
netflix_language_high_tvshows.head(5)


fig = px.bar(x = netflix_language_high_tvshows['Language'][:15],
             y = netflix_language_high_tvshows['Netflix'][:15], 
             color = netflix_language_high_tvshows['Netflix'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Language', 'x' : 'TV Shows Count'},
             title  = 'Language with Highest TV Shows : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


# hulu_language_tvshows = language_data_tvshows[language_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_language_tvshows = hulu_language_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
hulu_language_high_tvshows = df_language_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_language_high_tvshows = hulu_language_high_tvshows.drop(['index'], axis = 1)
 
hulu_language_low_tvshows = df_language_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_language_low_tvshows = hulu_language_low_tvshows.drop(['index'], axis = 1)
 
hulu_language_high_tvshows.head(5)


fig = px.bar(x = hulu_language_high_tvshows['Language'][:15],
             y = hulu_language_high_tvshows['Hulu'][:15], 
             color = hulu_language_high_tvshows['Hulu'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Language', 'x' : 'TV Shows Count'},
             title  = 'Language with Highest TV Shows : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


# prime_video_language_tvshows = language_data_tvshows[language_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_language_tvshows = prime_video_language_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
 
prime_video_language_high_tvshows = df_language_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_language_high_tvshows = prime_video_language_high_tvshows.drop(['index'], axis = 1)
 
prime_video_language_low_tvshows = df_language_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_language_low_tvshows = prime_video_language_low_tvshows.drop(['index'], axis = 1)
 
prime_video_language_high_tvshows.head(5)


fig = px.bar(x = prime_video_language_high_tvshows['Language'][:15],
             y = prime_video_language_high_tvshows['Prime Video'][:15], 
             color = prime_video_language_high_tvshows['Prime Video'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Language', 'x' : 'TV Shows Count'},
             title  = 'Language with Highest TV Shows : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


# disney_language_tvshows = language_data_tvshows[language_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_language_tvshows = disney_language_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
 
disney_language_high_tvshows = df_language_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_language_high_tvshows = disney_language_high_tvshows.drop(['index'], axis = 1)
 
disney_language_low_tvshows = df_language_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_language_low_tvshows = disney_language_low_tvshows.drop(['index'], axis = 1)
 
disney_language_high_tvshows.head(5)


fig = px.bar(x = disney_language_high_tvshows['Language'][:15],
             y = disney_language_high_tvshows['Disney+'][:15], 
             color = disney_language_high_tvshows['Disney+'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Language', 'x' : 'TV Shows Count'},
             title  = 'Language with Highest TV Shows : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(language_data_tvshows['TV Shows Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(language_data_tvshows['TV Shows Count'], ax = ax[1])
plt.show()


# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_language_tvshows = language_data_tvshows[language_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_language_tvshows = netflix_language_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)

hulu_language_tvshows = language_data_tvshows[language_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_language_tvshows = hulu_language_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)

prime_video_language_tvshows = language_data_tvshows[language_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_language_tvshows = prime_video_language_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)

disney_language_tvshows = language_data_tvshows[language_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_language_tvshows = disney_language_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Language TV Shows Count Per Platform')
 
# Plotting the information from each dataset into a histogram
 
sns.histplot(disney_language_tvshows['Disney+'][:50], color = 'darkblue', legend = True, kde = True)  
sns.histplot(prime_video_language_tvshows['Prime Video'][:50], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_language_tvshows['Netflix'][:50], color = 'red', legend = True, kde = True)
sns.histplot(hulu_language_tvshows['Hulu'][:50], color = 'lightgreen', legend = True, kde = True)                                
 
# Setting the legend
plt.legend(['Disney+', 'Prime Video', 'Netflix', 'Hulu'])
plt.show()


print(f'''
      The Language with Highest TV Shows Count Ever Got is '{df_language_high_tvshows['Language'][0]}' : '{df_language_high_tvshows['TV Shows Count'].max()}'\n
      The Language with Lowest TV Shows Count Ever Got is '{df_language_low_tvshows['Language'][0]}' : '{df_language_low_tvshows['TV Shows Count'].min()}'\n
      
      The Language with Highest TV Shows Count on 'Netflix' is '{netflix_language_high_tvshows['Language'][0]}' : '{netflix_language_high_tvshows['Netflix'].max()}'\n
      The Language with Lowest TV Shows Count on 'Netflix' is '{netflix_language_low_tvshows['Language'][0]}' : '{netflix_language_low_tvshows['Netflix'].min()}'\n
      
      The Language with Highest TV Shows Count on 'Hulu' is '{hulu_language_high_tvshows['Language'][0]}' : '{hulu_language_high_tvshows['Hulu'].max()}'\n
      The Language with Lowest TV Shows Count on 'Hulu' is '{hulu_language_low_tvshows['Language'][0]}' : '{hulu_language_low_tvshows['Hulu'].min()}'\n
      
      The Language with Highest TV Shows Count on 'Prime Video' is '{prime_video_language_high_tvshows['Language'][0]}' : '{prime_video_language_high_tvshows['Prime Video'].max()}'\n
      The Language with Lowest TV Shows Count on 'Prime Video' is '{prime_video_language_low_tvshows['Language'][0]}' : '{prime_video_language_low_tvshows['Prime Video'].min()}'\n
      
      The Language with Highest TV Shows Count on 'Disney+' is '{disney_language_high_tvshows['Language'][0]}' : '{disney_language_high_tvshows['Disney+'].max()}'\n
      The Language with Lowest TV Shows Count on 'Disney+' is '{disney_language_low_tvshows['Language'][0]}' : '{disney_language_low_tvshows['Disney+'].min()}'\n 
      ''')

      The Language with Highest TV Shows Count Ever Got is 'English' : '3876'

      The Language with Lowest TV Shows Count Ever Got is ' Ancient (to 1453)' : '1'

      
      The Language with Highest TV Shows Count on 'Netflix' is 'English' : '1177'

      The Language with Lowest TV Shows Count on 'Netflix' is ' Ancient (to 1453)' : '0'

      
      The Language with Highest TV Shows Count on 'Hulu' is 'English' : '1271'

      The Language with Lowest TV Shows Count on 'Hulu' is 'Kurdish' : '0'

      
      The Language with Highest TV Shows Count on 'Prime Video' is 'English' : '1575'

      The Language with Lowest TV Shows Count on 'Prime Video' is 'Kazakh' : '0'

      
      The Language with Highest TV Shows Count on 'Disney+' is 'English' : '166'

      The Language with Lowest TV Shows Count on 'Disney+' is 'Scottish Gaelic' : '0'


# Distribution of tvshows language in each platform
plt.figure(figsize = (20, 5))
plt.title('Language with TV Shows Count for All Platforms')
sns.violinplot(x = language_data_tvshows['TV Shows Count'][:100], color = 'gold', legend = True, kde = True, shade = False)
plt.show()


# Distribution of Language TV Shows Count in each platform
f1, ax1 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = netflix_language_tvshows['Netflix'][:100], color = 'red', ax = ax1[0])
sns.violinplot(x = hulu_language_tvshows['Hulu'][:100], color = 'lightgreen', ax = ax1[1])
 
f2, ax2 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = prime_video_language_tvshows['Prime Video'][:100], color = 'lightblue', ax = ax2[0])
sns.violinplot(x = disney_language_tvshows['Disney+'][:100], color = 'darkblue', ax = ax2[1])
plt.show()


print(f'''
      Accross All Platforms the Average TV Shows Count of Language is '{round(language_data_tvshows['TV Shows Count'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Language on 'Netflix' is '{round(netflix_language_tvshows['Netflix'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Language on 'Hulu' is '{round(hulu_language_tvshows['Hulu'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Language on 'Prime Video' is '{round(prime_video_language_tvshows['Prime Video'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Language on 'Disney+' is '{round(disney_language_tvshows['Disney+'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average TV Shows Count of Language is '57.03'

      The Average TV Shows Count of Language on 'Netflix' is '28.95'

      The Average TV Shows Count of Language on 'Hulu' is '31.33'

      The Average TV Shows Count of Language on 'Prime Video' is '32.92'

      The Average TV Shows Count of Language on 'Disney+' is '7.09'


print(f'''
      Accross All Platforms Total Count of Language is '{language_data_tvshows['Language'].unique().shape[0]}'\n
      Total Count of Language on 'Netflix' is '{netflix_language_tvshows['Language'].unique().shape[0]}'\n
      Total Count of Language on 'Hulu' is '{hulu_language_tvshows['Language'].unique().shape[0]}'\n
      Total Count of Language on 'Prime Video' is '{prime_video_language_tvshows['Language'].unique().shape[0]}'\n
      Total Count of Language on 'Disney+' is '{disney_language_tvshows['Language'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Language is '101'

      Total Count of Language on 'Netflix' is '73'

      Total Count of Language on 'Hulu' is '58'

      Total Count of Language on 'Prime Video' is '64'

      Total Count of Language on 'Disney+' is '32'


plt.figure(figsize = (20, 5))
sns.lineplot(x = language_data_tvshows['Language'][:10], y = language_data_tvshows['Netflix'][:10], color = 'red')
sns.lineplot(x = language_data_tvshows['Language'][:10], y = language_data_tvshows['Hulu'][:10], color = 'lightgreen')
sns.lineplot(x = language_data_tvshows['Language'][:10], y = language_data_tvshows['Prime Video'][:10], color = 'lightblue')
sns.lineplot(x = language_data_tvshows['Language'][:10], y = language_data_tvshows['Disney+'][:10], color = 'darkblue')
plt.xlabel('Language', fontsize = 20)
plt.ylabel('TV Shows Count', fontsize = 20)
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
 
n_l_ax1 = sns.lineplot(y = language_data_tvshows['Language'][:10], x = language_data_tvshows['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_l_ax2 = sns.lineplot(y = language_data_tvshows['Language'][:10], x = language_data_tvshows['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_l_ax3 = sns.lineplot(y = language_data_tvshows['Language'][:10], x = language_data_tvshows['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_l_ax4 = sns.lineplot(y = language_data_tvshows['Language'][:10], x = language_data_tvshows['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_l_ax1.title.set_text(labels[0])
h_l_ax2.title.set_text(labels[1])
p_l_ax3.title.set_text(labels[2])
d_l_ax4.title.set_text(labels[3])
 
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_l_ax1 = sns.barplot(y = netflix_language_tvshows['Language'][:10], x = netflix_language_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_l_ax2 = sns.barplot(y = hulu_language_tvshows['Language'][:10], x = hulu_language_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_l_ax3 = sns.barplot(y = prime_video_language_tvshows['Language'][:10], x = prime_video_language_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_l_ax4 = sns.barplot(y = disney_language_tvshows['Language'][:10], x = disney_language_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_l_ax1.title.set_text(labels[0])
h_l_ax2.title.set_text(labels[1])
p_l_ax3.title.set_text(labels[2])
d_l_ax4.title.set_text(labels[3])
 
plt.show()


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Language  TV Shows Count Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_language_tvshows['Netflix'][:10], color = 'red', legend = True)
sns.kdeplot(hulu_language_tvshows['Hulu'][:10], color = 'green', legend = True)
sns.kdeplot(prime_video_language_tvshows['Prime Video'][:10], color = 'lightblue', legend = True)
sns.kdeplot(disney_language_tvshows['Disney+'][:10], color = 'darkblue', legend = True)                                      
                                      
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_l_ax1 = sns.barplot(y = language_data_tvshows['Language'][:10], x = language_data_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_l_ax2 = sns.barplot(y = language_data_tvshows['Language'][:10], x = language_data_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_l_ax3 = sns.barplot(y = language_data_tvshows['Language'][:10], x = language_data_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_l_ax4 = sns.barplot(y = language_data_tvshows['Language'][:10], x = language_data_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_l_ax1.title.set_text(labels[0])
h_l_ax2.title.set_text(labels[1])
p_l_ax3.title.set_text(labels[2])
d_l_ax4.title.set_text(labels[3])
 
plt.show()


df_tvshows_mixed_languages.drop(df_tvshows_mixed_languages.loc[df_tvshows_mixed_languages['Language'] == "NA"].index, inplace = True)
# df_tvshows_mixed_languages = df_tvshows_mixed_languages[df_tvshows_mixed_languages.Language != "NA"]
df_tvshows_mixed_languages.drop(df_tvshows_mixed_languages.loc[df_tvshows_mixed_languages['Number of Languages'] == 1].index, inplace = True)


df_tvshows_mixed_languages.head(5)


mixed_languages_count = df_tvshows_mixed_languages.groupby('Language')['Title'].count()
mixed_languages_tvshows = df_tvshows_mixed_languages.groupby('Language')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
mixed_languages_data_tvshows = pd.concat([mixed_languages_count, mixed_languages_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count', 'Language' : 'Mixed Language'})
mixed_languages_data_tvshows = mixed_languages_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)


mixed_languages_data_tvshows.head(5)


# Mixed Language with TV Shows Counts - All Platforms Combined
mixed_languages_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)[:10]


df_mixed_languages_high_tvshows = mixed_languages_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_mixed_languages_high_tvshows = df_mixed_languages_high_tvshows.drop(['index'], axis = 1)
# filter = (mixed_languages_data_tvshows['TV Shows Count'] = =  (mixed_languages_data_tvshows['TV Shows Count'].max()))
# df_mixed_languages_high_tvshows = mixed_languages_data_tvshows[filter]
 
# highest_rated_tvshows = mixed_languages_data_tvshows.loc[mixed_languages_data_tvshows['TV Shows Count'].idxmax()]
 
print('\nMixed Language with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_mixed_languages_high_tvshows.head(5)

Mixed Language with Highest Ever TV Shows Count are : All Platforms Combined


fig = px.bar(y = df_mixed_languages_high_tvshows['Mixed Language'][:15],
             x = df_mixed_languages_high_tvshows['TV Shows Count'][:15], 
             color = df_mixed_languages_high_tvshows['TV Shows Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Mixed Language'},
             title  = 'TV Shows with Highest Number of Mixed Languages : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_mixed_languages_low_tvshows = mixed_languages_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_mixed_languages_low_tvshows = df_mixed_languages_low_tvshows.drop(['index'], axis = 1)
# filter = (mixed_languages_data_tvshows['TV Shows Count'] = =  (mixed_languages_data_tvshows['TV Shows Count'].min()))
# df_mixed_languages_low_tvshows = mixed_languages_data_tvshows[filter]
 
print('\nMixed Language with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_mixed_languages_low_tvshows.head(5)

Mixed Language with Lowest Ever TV Shows Count are : All Platforms Combined


fig = px.bar(y = df_mixed_languages_low_tvshows['Mixed Language'][:15],
             x = df_mixed_languages_low_tvshows['TV Shows Count'][:15], 
             color = df_mixed_languages_low_tvshows['TV Shows Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Mixed Language'},
             title  = 'TV Shows with Lowest Number of Mixed Languages : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      Total '{df_tvshows_languages['Language'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see TV Shows from Total '{mixed_languages_data_tvshows['Mixed Language'].unique().shape[0]}' Mixed Language, They were Like this, \n
 
      {mixed_languages_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['Mixed Language'].head(5).unique()} etc. \n
 
      The Mixed Language with Highest TV Shows Count have '{mixed_languages_data_tvshows['TV Shows Count'].max()}' TV Shows Available is '{df_mixed_languages_high_tvshows['Mixed Language'][0]}', &\n
      The Mixed Language with Lowest TV Shows Count have '{mixed_languages_data_tvshows['TV Shows Count'].min()}' TV Shows Available is '{df_mixed_languages_low_tvshows['Mixed Language'][0]}'
      ''')

      Total '4794' Titles are available on All Platforms, out of which

      You Can Choose to see TV Shows from Total '245' Mixed Language, They were Like this, 

 
      ['Japanese,English' 'English,Spanish' 'English,French' 'English,Japanese'
 'Spanish,English'] etc. 

 
      The Mixed Language with Highest TV Shows Count have '67' TV Shows Available is 'Japanese,English', &

      The Mixed Language with Lowest TV Shows Count have '1' TV Shows Available is 'Japanese,English,Korean,Hindi'


fig = px.pie(mixed_languages_data_tvshows[:10], names = 'Mixed Language', values = 'TV Shows Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'TV Shows Count based on Mixed Language')
fig.show()


# netflix_mixed_languages_tvshows = mixed_languages_data_tvshows[mixed_languages_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_mixed_languages_tvshows = netflix_mixed_languages_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
netflix_mixed_languages_high_tvshows = df_mixed_languages_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_languages_high_tvshows = netflix_mixed_languages_high_tvshows.drop(['index'], axis = 1)
 
netflix_mixed_languages_low_tvshows = df_mixed_languages_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_mixed_languages_low_tvshows = netflix_mixed_languages_low_tvshows.drop(['index'], axis = 1)
 
netflix_mixed_languages_high_tvshows.head(5)


# hulu_mixed_languages_tvshows = mixed_languages_data_tvshows[mixed_languages_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_mixed_languages_tvshows = hulu_mixed_languages_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
hulu_mixed_languages_high_tvshows = df_mixed_languages_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_languages_high_tvshows = hulu_mixed_languages_high_tvshows.drop(['index'], axis = 1)
 
hulu_mixed_languages_low_tvshows = df_mixed_languages_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_mixed_languages_low_tvshows = hulu_mixed_languages_low_tvshows.drop(['index'], axis = 1)
 
hulu_mixed_languages_high_tvshows.head(5)


# prime_video_mixed_languages_tvshows = mixed_languages_data_tvshows[mixed_languages_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_mixed_languages_tvshows = prime_video_mixed_languages_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
 
prime_video_mixed_languages_high_tvshows = df_mixed_languages_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_languages_high_tvshows = prime_video_mixed_languages_high_tvshows.drop(['index'], axis = 1)
 
prime_video_mixed_languages_low_tvshows = df_mixed_languages_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_mixed_languages_low_tvshows = prime_video_mixed_languages_low_tvshows.drop(['index'], axis = 1)
 
prime_video_mixed_languages_high_tvshows.head(5)


# disney_mixed_languages_tvshows = mixed_languages_data_tvshows[mixed_languages_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_mixed_languages_tvshows = disney_mixed_languages_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
 
disney_mixed_languages_high_tvshows = df_mixed_languages_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_languages_high_tvshows = disney_mixed_languages_high_tvshows.drop(['index'], axis = 1)
 
disney_mixed_languages_low_tvshows = df_mixed_languages_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_mixed_languages_low_tvshows = disney_mixed_languages_low_tvshows.drop(['index'], axis = 1)
 
disney_mixed_languages_high_tvshows.head(5)


f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(mixed_languages_data_tvshows['TV Shows Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(mixed_languages_data_tvshows['TV Shows Count'], ax = ax[1])
plt.show()


# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_mixed_languages_tvshows = mixed_languages_data_tvshows[mixed_languages_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_languages_tvshows = netflix_mixed_languages_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)

hulu_mixed_languages_tvshows = mixed_languages_data_tvshows[mixed_languages_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_languages_tvshows = hulu_mixed_languages_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)

prime_video_mixed_languages_tvshows = mixed_languages_data_tvshows[mixed_languages_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_languages_tvshows = prime_video_mixed_languages_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)

disney_mixed_languages_tvshows = mixed_languages_data_tvshows[mixed_languages_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_languages_tvshows = disney_mixed_languages_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Language TV Shows Count Per Platform')
 
# Plotting the information from each dataset into a histogram
 
sns.histplot(prime_video_mixed_languages_tvshows['Prime Video'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_mixed_languages_tvshows['Netflix'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_mixed_languages_tvshows['Hulu'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_mixed_languages_tvshows['Disney+'][:100], color = 'darkblue', legend = True, kde = True)                                
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()


print(f'''
      The Mixed Language with Highest TV Shows Count Ever Got is '{df_mixed_languages_high_tvshows['Mixed Language'][0]}' : '{df_mixed_languages_high_tvshows['TV Shows Count'].max()}'\n
      The Mixed Language with Lowest TV Shows Count Ever Got is '{df_mixed_languages_low_tvshows['Mixed Language'][0]}' : '{df_mixed_languages_low_tvshows['TV Shows Count'].min()}'\n
      
      The Mixed Language with Highest TV Shows Count on 'Netflix' is '{netflix_mixed_languages_high_tvshows['Mixed Language'][0]}' : '{netflix_mixed_languages_high_tvshows['Netflix'].max()}'\n
      The Mixed Language with Lowest TV Shows Count on 'Netflix' is '{netflix_mixed_languages_low_tvshows['Mixed Language'][0]}' : '{netflix_mixed_languages_low_tvshows['Netflix'].min()}'\n
      
      The Mixed Language with Highest TV Shows Count on 'Hulu' is '{hulu_mixed_languages_high_tvshows['Mixed Language'][0]}' : '{hulu_mixed_languages_high_tvshows['Hulu'].max()}'\n
      The Mixed Language with Lowest TV Shows Count on 'Hulu' is '{hulu_mixed_languages_low_tvshows['Mixed Language'][0]}' : '{hulu_mixed_languages_low_tvshows['Hulu'].min()}'\n
      
      The Mixed Language with Highest TV Shows Count on 'Prime Video' is '{prime_video_mixed_languages_high_tvshows['Mixed Language'][0]}' : '{prime_video_mixed_languages_high_tvshows['Prime Video'].max()}'\n
      The Mixed Language with Lowest TV Shows Count on 'Prime Video' is '{prime_video_mixed_languages_low_tvshows['Mixed Language'][0]}' : '{prime_video_mixed_languages_low_tvshows['Prime Video'].min()}'\n
      
      The Mixed Language with Highest TV Shows Count on 'Disney+' is '{disney_mixed_languages_high_tvshows['Mixed Language'][0]}' : '{disney_mixed_languages_high_tvshows['Disney+'].max()}'\n
      The Mixed Language with Lowest TV Shows Count on 'Disney+' is '{disney_mixed_languages_low_tvshows['Mixed Language'][0]}' : '{disney_mixed_languages_low_tvshows['Disney+'].min()}'\n 
      ''')

      The Mixed Language with Highest TV Shows Count Ever Got is 'Japanese,English' : '67'

      The Mixed Language with Lowest TV Shows Count Ever Got is 'Japanese,English,Korean,Hindi' : '1'

      
      The Mixed Language with Highest TV Shows Count on 'Netflix' is 'Japanese,English' : '21'

      The Mixed Language with Lowest TV Shows Count on 'Netflix' is 'English,Dutch,French,German,Lithuanian' : '0'

      
      The Mixed Language with Highest TV Shows Count on 'Hulu' is 'Japanese,English' : '54'

      The Mixed Language with Lowest TV Shows Count on 'Hulu' is 'English,Egyptian (Ancient),Russian,Latin,Arabic,Japanese' : '0'

      
      The Mixed Language with Highest TV Shows Count on 'Prime Video' is 'English,French' : '13'

      The Mixed Language with Lowest TV Shows Count on 'Prime Video' is 'English,Egyptian (Ancient),Russian,Latin,Arabic,Japanese' : '0'

      
      The Mixed Language with Highest TV Shows Count on 'Disney+' is 'English,Korean' : '3'

      The Mixed Language with Lowest TV Shows Count on 'Disney+' is 'Japanese,English' : '0'


print(f'''
      Accross All Platforms the Average TV Shows Count of Mixed Language is '{round(mixed_languages_data_tvshows['TV Shows Count'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Mixed Language on 'Netflix' is '{round(netflix_mixed_languages_tvshows['Netflix'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Mixed Language on 'Hulu' is '{round(hulu_mixed_languages_tvshows['Hulu'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Mixed Language on 'Prime Video' is '{round(prime_video_mixed_languages_tvshows['Prime Video'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Mixed Language on 'Disney+' is '{round(disney_mixed_languages_tvshows['Disney+'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average TV Shows Count of Mixed Language is '1.93'

      The Average TV Shows Count of Mixed Language on 'Netflix' is '1.57'

      The Average TV Shows Count of Mixed Language on 'Hulu' is '2.28'

      The Average TV Shows Count of Mixed Language on 'Prime Video' is '1.54'

      The Average TV Shows Count of Mixed Language on 'Disney+' is '1.23'


print(f'''
      Accross All Platforms Total Count of Mixed Language is '{mixed_languages_data_tvshows['Mixed Language'].unique().shape[0]}'\n
      Total Count of Mixed Language on 'Netflix' is '{netflix_mixed_languages_tvshows['Mixed Language'].unique().shape[0]}'\n
      Total Count of Mixed Language on 'Hulu' is '{hulu_mixed_languages_tvshows['Mixed Language'].unique().shape[0]}'\n
      Total Count of Mixed Language on 'Prime Video' is '{prime_video_mixed_languages_tvshows['Mixed Language'].unique().shape[0]}'\n
      Total Count of Mixed Language on 'Disney+' is '{disney_mixed_languages_tvshows['Mixed Language'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Mixed Language is '245'

      Total Count of Mixed Language on 'Netflix' is '134'

      Total Count of Mixed Language on 'Hulu' is '74'

      Total Count of Mixed Language on 'Prime Video' is '97'

      Total Count of Mixed Language on 'Disney+' is '13'


plt.figure(figsize = (20, 5))
sns.lineplot(x = mixed_languages_data_tvshows['Mixed Language'][:5], y = mixed_languages_data_tvshows['Netflix'][:5], color = 'red')
sns.lineplot(x = mixed_languages_data_tvshows['Mixed Language'][:5], y = mixed_languages_data_tvshows['Hulu'][:5], color = 'lightgreen')
sns.lineplot(x = mixed_languages_data_tvshows['Mixed Language'][:5], y = mixed_languages_data_tvshows['Prime Video'][:5], color = 'lightblue')
sns.lineplot(x = mixed_languages_data_tvshows['Mixed Language'][:5], y = mixed_languages_data_tvshows['Disney+'][:5], color = 'darkblue')
plt.xlabel('Mixed Language', fontsize = 15)
plt.ylabel('TV Shows Count', fontsize = 15)
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_l_ax1 = sns.barplot(y = mixed_languages_data_tvshows['Mixed Language'][:10], x = mixed_languages_data_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_l_ax2 = sns.barplot(y = mixed_languages_data_tvshows['Mixed Language'][:10], x = mixed_languages_data_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_l_ax3 = sns.barplot(y = mixed_languages_data_tvshows['Mixed Language'][:10], x = mixed_languages_data_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_l_ax4 = sns.barplot(y = mixed_languages_data_tvshows['Mixed Language'][:10], x = mixed_languages_data_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_l_ax1.title.set_text(labels[0])
h_l_ax2.title.set_text(labels[1])
p_l_ax3.title.set_text(labels[2])
d_l_ax4.title.set_text(labels[3])
 
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
 
n_ml_ax1 = sns.lineplot(y = mixed_languages_data_tvshows['Mixed Language'][:10], x = mixed_languages_data_tvshows['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_ml_ax2 = sns.lineplot(y = mixed_languages_data_tvshows['Mixed Language'][:10], x = mixed_languages_data_tvshows['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_ml_ax3 = sns.lineplot(y = mixed_languages_data_tvshows['Mixed Language'][:10], x = mixed_languages_data_tvshows['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_ml_ax4 = sns.lineplot(y = mixed_languages_data_tvshows['Mixed Language'][:10], x = mixed_languages_data_tvshows['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ml_ax1.title.set_text(labels[0])
h_ml_ax2.title.set_text(labels[1])
p_ml_ax3.title.set_text(labels[2])
d_ml_ax4.title.set_text(labels[3])
 
plt.show()


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Language  TV Shows Count Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_mixed_languages_tvshows['Netflix'][:50], color = 'red', legend = True)
sns.kdeplot(hulu_mixed_languages_tvshows['Hulu'][:50], color = 'green', legend = True)
sns.kdeplot(prime_video_mixed_languages_tvshows['Prime Video'][:50], color = 'lightblue', legend = True)
sns.kdeplot(disney_mixed_languages_tvshows['Disney+'][:50], color = 'darkblue', legend = True)                                      

# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_ml_ax1 = sns.barplot(y = netflix_mixed_languages_tvshows['Mixed Language'][:10], x = netflix_mixed_languages_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_ml_ax2 = sns.barplot(y = hulu_mixed_languages_tvshows['Mixed Language'][:10], x = hulu_mixed_languages_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_ml_ax3 = sns.barplot(y = prime_video_mixed_languages_tvshows['Mixed Language'][:10], x = prime_video_mixed_languages_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_ml_ax4 = sns.barplot(y = disney_mixed_languages_tvshows['Mixed Language'][:10], x = disney_mixed_languages_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ml_ax1.title.set_text(labels[0])
h_ml_ax2.title.set_text(labels[1])
p_ml_ax3.title.set_text(labels[2])
d_ml_ax4.title.set_text(labels[3])
 
plt.show()


fig = go.Figure(go.Funnel(y = mixed_languages_data_tvshows['Mixed Language'][:10], x = mixed_languages_data_tvshows['TV Shows Count'][:10]))
fig.show()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	Language	Plotline	Runtime	Kind	Seasons	Netflix	Prime Video	Disney+	Type
0	1	Snowpiercer	2013	18+	6.9	94%	NaN	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	English	Set seven years after the world has become a f...	60.0	tv series	3.0	1	0	0	1
1	2	Philadelphia	1993	13+	8.8	80%	NaN	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	English	The gang, 5 raging alcoholic, narcissists run ...	22.0	tv series	18.0	1	0	0	1
2	3	Roma	2018	18+	8.7	93%	NaN	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	English	In this British historical drama, the turbulen...	52.0	tv series	2.0	1	0	0	1
3	4	Amy	2015	18+	7.0	87%	NaN	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	English	A family drama focused on three generations of...	60.0	tv series	6.0	1	1	1	1
4	5	The Young Offenders	2016	NaN	8.0	100%	NaN	Alex Murphy,Chris Walley,Hilary Rose,Dominic M...	Comedy	United Kingdom,Ireland	English	NaN	30.0	tv series	3.0	1	0	0	1

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
count	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.0
mean	2716.500000	2010.668446	0.341311	0.293999	0.403351	0.033689	1.0
std	1568.227662	11.726176	0.474193	0.455633	0.490615	0.180445	0.0
min	1.000000	1901.000000	0.000000	0.000000	0.000000	0.000000	1.0
25%	1358.750000	2009.000000	0.000000	0.000000	0.000000	0.000000	1.0
50%	2716.500000	2014.000000	0.000000	0.000000	0.000000	0.000000	1.0
75%	4074.250000	2017.000000	1.000000	1.000000	1.000000	0.000000	1.0
max	5432.000000	2020.000000	1.000000	1.000000	1.000000	1.000000	1.0

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
ID	1.000000	-0.031346	-0.646330	0.034293	0.441264	0.195409	NaN
Year	-0.031346	1.000000	0.222316	-0.065807	-0.198675	-0.022741	NaN
Netflix	-0.646330	0.222316	1.000000	-0.366515	-0.515086	-0.119344	NaN
Hulu	0.034293	-0.065807	-0.366515	1.000000	-0.377374	-0.075701	NaN
Prime Video	0.441264	-0.198675	-0.515086	-0.377374	1.000000	-0.151442	NaN
Disney+	0.195409	-0.022741	-0.119344	-0.075701	-0.151442	1.000000	NaN
Type	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Runtime	Kind	Seasons	Netflix	Hulu	Disney+	Type	Service Provider	Number of Languages
0	2262	The Simpsons	1989	16	8.6	85	NA	Dan Castellaneta,Nancy Cartwright,Harry Sheare...	Animation,Comedy	United States	...	22	tv series	34	0	1	1	1	Disney+	20
1	1680	Legend Quest	2017	7	7.4	NA	NA	Johnny Rose,Annemarie Blanco,Oscar Cheda,Paul ...	Animation,Adventure,Comedy,Fantasy,Mystery	Mexico	...	NA	tv series	1	1	0	0	1	Netflix	18
2	5302	One Strange Rock	2018	7	8.8	83	NA	Will Smith,Chris Hadfield,Jerry Linenger,Mae C...	Documentary	United States	...	47	tv series	2	0	0	1	1	Disney+	16
3	921	Clannad	2007	7	7.9	NA	NA	Yûichi Nakamura,David Matranga,Mai Nakahara,Lu...	Animation,Comedy,Drama,Fantasy,Romance	Japan	...	30	tv series	1	1	1	0	1	Netflix	14
4	2293	Elementary	2012	16	7.9	95	NA	Jonny Lee Miller,Lucy Liu,Aidan Quinn,Jon Mich...	Crime,Drama,Mystery	United States	...	60	tv series	7	0	1	0	1	Hulu	12

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Runtime	Kind	Seasons	Hulu	Prime Video	Type	Service Provider	Number of Languages
0	2260	Vikings	2013	18	8.5	93	NA	Katheryn Winnick,Gustaf Skarsgård,Alexander Lu...	Action,Adventure,Drama,History,Romance,War	Ireland,Canada	...	44	tv series	6	1	1	1	Prime Video	10
1	346	24 Hours	2014	NR	8.3	86	NA	Kiefer Sutherland,Mary Lynn Rajskub,Carlos Ber...	Action,Crime,Drama,Thriller	United States	...	44	tv series	8	0	1	1	Prime Video	8
2	364	Carlos el terrorista	1980	NR	7.6	NA	NA	Edgar Ramírez,Alexander Scheer,Fadi Abi Samra,...	Biography,Crime,Drama,Thriller	France,Germany	...	334	tv series	1	0	1	1	Prime Video	8
3	4410	Marco Polo	2007	18	8	66	NA	Lorenzo Richelmy,Benedict Wong,Joan Chen,Remy ...	Adventure,Drama,History	United States	...	60	tv series	2	0	1	1	Prime Video	8
4	3743	Tom Clancy's Jack Ryan	2018	16	8.1	71	NA	John Krasinski,Wendell Pierce,John Hoogenakker...	Action,Drama,Thriller	United States	...	60	tv series	3	0	1	1	Prime Video	8

	Language	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
28	English	3876	1177	1271	1575	166
46	Japanese	402	142	224	95	4
82	Spanish	268	151	76	49	8
33	French	159	75	34	61	5
50	Korean	156	106	26	37	3
35	German	92	34	17	37	6
58	Mandarin	90	68	10	14	1
75	Russian	80	32	17	36	2
8	Arabic	62	31	16	17	2
45	Italian	57	32	12	16	2

	Language	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Ancient (to 1453)	1	0	1	1	0
1	Abkhazian	1	0	0	0	1
2	Wolof	1	0	0	1	0
3	Amharic	1	1	0	0	0
4	Aramaic	1	0	0	1	0

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Runtime	Kind	Seasons	Netflix	Prime Video	Type	Service Provider	Number of Languages
15	16	Eli	2019	18	7.9	46	NA	Sacha Baron Cohen,Hadar Ratzon Rotem,Yael Eita...	Drama,History	France	...	53	tv series	1	1	1	1	Netflix	2
28	29	A Love Story	2007	NR	6.1	74	NA	Seçkin Özdemir,Damla Sönmez,Yamaç Telli,Elçin ...	Drama,Romance	Turkey	...	100	tv series	1	1	0	1	Netflix	2
40	41	Heirs	2015	NR	7.5	67	NA	Lee Min-Ho,Park Shin-Hye,Woo-bin Kim,Kim Ji-Wo...	Comedy,Drama,Romance	South Korea	...	55	tv series	1	1	0	1	Netflix	2
61	62	Mahabharat	2013	NR	8.8	NA	NA	Saurabh Jain,Shaheer Sheikh,Pooja Sharma,Arav ...	Drama,War	India	...	20	tv series	1	1	0	1	Netflix	2
64	65	All About Asado	2016	NR	6.6	NA	Tony Bueno,Emily Pattison	Abby Harrison	Talk-Show	United States	...	89	tv series	NA	1	0	1	Netflix	3

	Mixed Language	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
176	Japanese,English	67	21	54	11	0
44	English,French	25	11	4	13	0
111	English,Spanish	25	10	12	7	1
79	English,Japanese	22	8	14	6	0
213	Spanish,English	11	6	5	1	1

	Mixed Language	TV Shows Count	Netflix	Hulu	Prime Video
0	Japanese,English,Korean,Hindi	1	1	0	0
1	English,French,Spanish	1	0	1	0
2	English,French,Spanish,Catalan,Russian,Polish,...	1	0	1	0
3	English,German,Arabic,Russian,French	1	0	0	1
4	English,German,French,Italian,Turkish	1	1	0	0

	Mixed Language	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	English,Korean	7	1	3	1	3
1	English,Czech,German	2	0	0	0	2
2	English,German	7	2	2	3	1
3	English,Spanish,Japanese	1	0	1	0	1
4	English,Spanish,Indonesian,Chinese,Arabic,Russ...	1	0	0	0	1